Forecasting is a very important function performed by businesses. Businesses need to be able to accurately forecast to manage current operations and plan for future growth or contraction. Businesses need to forecast expected sales as well as their expected expenses. In general, it is more important, and more difficult, to establish accurate revenue (sales) forecasts than expense forecasts because the external marketplace is generally much more volatile and unpredictable than the internal operations of the company doing the forecasting. For example, customers may accept new technologies at different rates than expected, competitors may change prices, or product launches may be delayed, all of which may affect future revenue. The ability to acquire and process frequent snapshots of forecast data allows companies to improve financial predictability and to increase organizational responsiveness.
For public companies, financial predictability is important for maintaining good external relations with investors. Business executives are experiencing increased scrutiny of their revenue forecasts. For example, chief financial officers (CFOs) are typically charged with ensuring that company performance meets or exceeds investment analysts' expectations. CFOs often provide “guidance” to analysts to raise or lower their estimates based on how well the CFO thinks the company will perform over a particular time period. Given the current volatility in the stock market, reactions to companies that do not meet expectations can cause severe changes in stock prices. In addition, the job security for many CFOs is often tied to their ability to accurately predict the performance of their business organizations.
Financial predictability also allows business organizations to respond quickly to changes in the market place. By rapidly refreshing revenue estimates, business managers can get a sense of how well their organization is performing. If their organization is performing well, business managers may decide to take operational actions such as raising prices. If their organization is performing poorly, business managers may try to motivate the sales force to increase sales. Making effective operational adjustments to business organizations is only possible if business managers have an accurate view of the marketplace provided by accurate forecasts.
In addition to taking operational actions, business managers may make resource allocation changes based on refreshed revenue forecasts. For example, business managers may decide to change headcount more quickly or slowly. As another example, business managers may decide to accelerate or delay expenditures tied to revenue projections, such as research and development projects. Thus, providing a forecasting mechanism and environment to help business organizations achieve financial predictability and organizational responsiveness is extremely valuable to companies.
There are two general impediments to business organizations achieving financial predictability. First, business organizations are composed of many different constituent groups with different perspectives on the future. Second, processes and systems are not in place to quickly collect and compare information. In most business organizations, the sales and marketing organizations influence the organization's perspective on what future revenues will be.
The sales organization is on the ground selling the products and has regular interaction with customers that is a valuable source of information about the future. For example, sales representatives may have the best information about when customer deals will close, how fast new products will be adopted, and how difficult it is to achieve a particular price point. The sales organization is usually given quotas by quarter and has a compensation structure that is largely based on achieving quotas, putting the focus clearly on the near-term. As a result, forecasts by the sales organization tend to be relatively accurate for the current quarter and possibly the next quarter, but are usually heavily discounted for time periods further out.
The marketing organization, which is sometimes referred to as the product or business unit organization, is responsible for inbound and outbound marketing. Inbound marketing is an assessment of customer needs and identification of target markets. Outbound marketing is the generation of marketing collateral, public relations and events. The marketing organization is sometimes also responsible for pricing and product strategy. Product marketers will often control when product launches occur. Therefore, the marketing organization knows a lot about macro market conditions that could affect revenue. Because marketing does not typically deal with individual customers on a day-to-day basis, marketing forecasts tend to be more valid over the medium and long-term. Product marketers may have revenue, gross profit, or even full responsibility for the profit and loss of their products, so they may have an important interest in forecasting financial performance.
For many businesses, the optimal revenue forecast is achieved by having the sales and marketing organizations agree upon an “official” or “consensus” revenue forecast. This consensus-generation practice (as opposed to taking just one forecast or the other, or by allowing separate forecasts to remain divergent) is often superior for two reasons. First, the process of achieving consensus forces constituents to reveal information and state implicit assumptions, resulting in a more informed forecast. Second, expenses must be driven off a coherent pro form a (forecast) profit and loss (P&L) statement. If managers in the company are spending based on divergent P&L forecasts, then the business organization's resources may not be coherently aligned towards an objective. Because the sales and marketing organizations often forecast slightly different ways, e.g., sales, forecasts all products in a given region while marketing forecasts all regions for given product, and because often the sales and marketing organizations are hierarchically deep and may only come together at the chief executive officer (CEO) level, getting the sales and marketing organizations to agree on official revenue forecast in a timely manner is a huge organizational challenge.
Based on industry, individual business practices, and organizational capabilities, companies reforecast revenue at frequencies ranging from once a year to real-time. As an example, in many companies, forecasting is organized into a set of processes often named the Annual Operating Plan (AOP), the Outlook process, and the Flash process. During AOP process, companies set their revenue, expense, and capital budget plans for the upcoming fiscal year. A major component of all of these processes is revenue forecasting.
Outlook processes are used to update and extend the forecast in the AOP, but usually in less detail. In the Outlook process, the group in the company responsible for Financial Planning and Analysis (FP&A) usually coordinates a rollup of forecasts from both the Sales organization and the Marketing organization. Often, both organizations are required to specify their forecasts in some level of dimensional detail. Usually this level of detail is revenue by product family (major product area) and region (e.g., Europe). In some situations, revenue forecasts are based upon bookings instead of revenue. Bookings are created when orders are taken while revenue may not be recognized until a product has been sent to the customer. Sales and marketing may be required to forecast revenue, bookings, or both.
Once the forecasts have been rolled up, the FP&A organization then conducts a meeting to identify and resolve discrepancies in the separate forecasts. This is often called an “alignment,” “sync-up,” or “convergence” meeting. The result of this meeting is the official revenue forecast that is provided to business managers to make operational and resource decisions. At some point in the process, either in the rollup or after the convergence meeting, forecasts based upon bookings are converted to a revenue basis.
The Flash process may be identical in format to the outlook process, but with a shorter time horizon (e.g., the current quarter only). More typically, only the forecast from the sales organization is rolled up and used as the official forecast, obviating the need for the consensus meeting.
Business organizations use a variety of systems and mechanisms to manage forecast data. One approach involves the use of spreadsheet-based solutions. Spreadsheet-based solutions generally involve the use of a patchwork of spreadsheets that are communicated between personnel via e-mail. Forecasts are generated in spreadsheets and are subsequently e-mailed to each level of the respective organizational hierarchies for review. Ultimately, a set of top-level spreadsheets are e-mailed to FP&A personnel in order to produce the Flash process forecasts or organize the consensus meeting for the Outlook process forecasts. The output of both processes is usually a set of spreadsheets that is e-mailed to various personnel within the business organization. Spreadsheet-based solutions are popular because spreadsheet application software is readily available, is inexpensive, is generally very flexible and is relatively easy to learn and use.
Despite the benefits provided by the use of spreadsheets, spreadsheet-based solutions have several drawbacks for use in managing forecast data. First, spreadsheets are error-prone. The free-form flexibility of a spreadsheet means that individual users can modify the fundamental spreadsheet structure, e.g., change column and row structures or edit or overwrite formulas, which makes consolidating forecasts from multiple individuals tricky and error-prone. Second, enforcing version control is difficult. Users of spreadsheets have the ability to save different versions to their local computers or on networks with any naming convention. As a result, business organizations must rely upon individual users to enforce their own version control through diligence in naming files and creating directories in which to store spreadsheet files. Third, consolidating spreadsheet data from multiple users can be extremely difficult and labor intensive. Individuals responsible for consolidating data must ensure that they have the most recent version of each spreadsheet and then must manually consolidate the data. Manual consolidation is complicated by non-standard formatting.
Another approach for managing forecast data involves the use of demand planning products. Demand planning software is primarily intended to help individual users generate statistically-based forecasts and then disaggregate forecasts into the very detailed level of information needed for manufacturing planning and supply chain management. One example of a demand planning product is i2 Technologies' Demand Planner. Demand planning products are difficult, if not impossible, to use for revenue forecasting for several reasons. First, demand planning products are mainly intended to provide unit volume demand forecasts to manufacturing organizations. This is because manufacturing organizations are primarily concerned with knowing the demand in unit volume for particular products. Consequently, many demand planning applications are poorly suited for tracking revenue information. Second, many demand planning products are designed for a single or small number of users. As described earlier, the revenue forecasting process involves collecting forecasts from an entire organizational hierarchy in the sales organization as well as collaboration among constituencies to arrive at a consensus. Systems that are designed primarily for use by a small set of “power users” often do not have good user interfaces. Furthermore, such systems do not have good aggregation mechanisms for collecting distributed forecast data.
Another solution to the problem of managing forecast data involves the use of sales force automation (SFA) applications to generate revenue forecast data. SFA applications are designed generally to help sales representatives track details of discrete sales opportunities. Despite their effectiveness in this regard, SFA applications have several significant drawbacks. One drawback of SFA applications generally do not use any input from marketing organizations and use short-term sales opportunity data. This occurs primarily because the sales pipeline tapers off over time since discrete sales opportunities will only be identified a certain amount of time in the future. The result is that just reporting on the sales pipeline is generally insufficient for revenue forecasting purposes.
Another drawback of SFA applications is that support for product detail is generally poor. For example, some SFA systems do not support the ability to specify products in an opportunity, but rather only the total amount of the deal. Those SFA systems that do support products only support a limited number of products. Supporting a limited number of products or product details makes financial forecasting difficult because adequate comparisons with marketing cannot be made. For example, marketers forecast by product, so without a corresponding sales forecast by product, comparisons other than at the total level are impossible. Also, insufficient product detail makes forecasting gross margin difficult since gross margin varies by product, and there is insufficient detail for manufacturing.
Based upon the need for business organizations to accurately and quickly manage and process forecast data, an approach for managing forecast data that does not suffer from the limitations of conventional approaches is highly desirable. There is a particular need for an approach for managing forecast data from a large number of disparate sources and for maintaining forecast data at product levels. There is a further need for an approach for controlling access to forecast data that does not suffer from limitations in prior approaches.